RESULT: only illuminate hybridized modes

scripts/ringdown_spectrum_analysis/004_simulation_11_07.py

@@ -60,7 +60,7 @@ def make_params_and_solve(
 
         case "hybrid_to_one_bath":
             params.drive_override = (
-                np.array([params.Ω * (1 - params.δ), params.Ω * params.δ * 2]),
+                np.array([params.Ω * (1 - params.δ), params.Ω * (1 + params.δ)]),
                 np.ones(2),
             )
 
@@ -163,7 +163,7 @@ def generate_phase_one_data(
     ax_realtime.set_title("Photo-diode AC Intensity")
 
     # now we plot the power spectrum
-    window = (float(params.laser_off_time or 0), t[-1])
+    window = (float(params.laser_off_time) or 0, t[-1])
     # window = (0, float(params.laser_off_time or 0))
 
     ringdown_params = RingdownParams(
@@ -260,3 +260,22 @@ if __name__ == "__main__":
     """,
     )
     save_figure(fig, "004_05_11_07_simulation_only_a_drive_on_bath")
+
+    fig = generate_phase_one_data(
+        laser_detuning=13 - 3.25,
+        g_0=0.3,
+        drive_mode="hybrid_to_one_bath",
+        off_factor=0.4,
+        noise=True,
+        yscale="log",
+        extra_title="""
+        The same as the first simulation, but with the laser at a bath mode and driving at Ω-δ,Ω+δ i.e coupling Bath-Hyb-Hyb-Bath.
+
+    From a simple analytical estimate, this can be seen as advantageous!
+
+    The log-scale on the spectrum is necessary as the bath mode gets way more excited! However the noise magnitude is the same as in the other simulations.
+
+    Timing is /not/ important here *if* the drive amplitude is low engough!
+    """,
+    )
+    save_figure(fig, "004_06_11_07_bhhb_best")

scripts/ringdown_spectrum_analysis/005_11_07_analysis_on_bath.py

@@ -0,0 +1,110 @@
+from rabifun.system import *
+from rabifun.plots import *
+from rabifun.utilities import *
+from rabifun.analysis import *
+from ringfit.data import ScanData
+from ringfit.plotting import *
+import gc
+
+path = "../../data/11_07_24/second_signal"
+scan = ScanData.from_dir(path, extension="npz")
+
+
+# %% plot scan
+gc.collect()
+fig = plt.figure("interactive", constrained_layout=True, figsize=(20, 3 * 5))
+fig.clf()
+(ax_signal, ax_window, ax_spectrum) = fig.subplot_mosaic("AA;BC").values()
+plot_scan(scan, ax=ax_signal, linewidth=0.5, every=1000)
+
+
+# %% window
+
+# here we select a step that is resonant with a hybridized peak and
+# then plot the full photodiode voltage trace and just the window
+
+T_step = 0.0002
+N = 100
+t_scan_peak = 0.0057815 + 0.1e-6  # T * N_steps
+t_scan_peak = 0.0057815 - 0.095e-6 + 28 * T_step  # T * N_steps
+t_peak = t_scan_peak + N * T_step
+win_length = 5e-05
+
+window = t_scan_peak, t_scan_peak + win_length * 0.2
+
+
+ax_signal.axvline(t_peak, color="r", linestyle="--")
+ax_signal.axvline(t_scan_peak, color="r", linestyle="--")
+ax_signal.axvspan(*window, color="r", linestyle="--")
+ax_signal.set_title("Full photodiode voltage trace")
+ax_signal.set_xlabel("Time [s]")
+ax_signal.set_ylabel("Voltage [arb]")
+
+mask = (scan.time > window[0]) & (scan.time < window[1])
+ax_window.clear()
+ax_window.plot(scan.time[mask], scan.output[mask], linewidth=0.1)
+ax_window.set_title("Windowed photodiode voltage trace")
+ax_window.set_xlabel("Time [s]")
+
+# %% peak analysis
+## herein we detect the pertinent peaks and refine them using a lorentzian fit
+ringdown_params = RingdownParams(
+    fω_shift=0,
+    mode_window=(0, 4),
+    fΩ_guess=12.9e6,
+    fδ_guess=0.2 * 12.9e6,
+    η_guess=0.5e6,
+    absolute_low_cutoff=5e6,
+)
+
+peak_info = find_peaks(scan, ringdown_params, window, prominence=0.1)
+peak_info = refine_peaks(peak_info, ringdown_params)
+plot_spectrum_and_peak_info(ax_spectrum, peak_info, ringdown_params, annotate=True)
+
+
+# %% hand-crafted interpretation
+b1, b2, b3 = peak_info.peak_freqs[[0, 1, 2]]
+Ω = b3 - b2
+hyb_freq = b1 - 0.18 * Ω
+alt_Ω = b2 - b1
+
+hyb_amp = peak_info.power[peak_info.peaks[1]] / (np.sqrt(2) * 5) ** 2 * 10
+hyb_width = peak_info.peak_widths[1] * 5
+
+ax_spectrum.plot(
+    peak_info.freq,
+    lorentzian(peak_info.freq, hyb_amp, hyb_freq, hyb_width),
+    color="C3",
+    label="hybridized mode?",
+)
+# %%
+ax_spectrum.legend()
+fig.suptitle(
+    f"""
+Analysis of the data from the 11/07.
+We modulated at the FSR (13MHz) and FSR-δ and 2δ.
+*Laser on first bath mode*
+
+Here the laser hits the first bathmode beside the hybidized modes. This is the reason
+why the peaks toward higher frequencies are split (we're detuned from the unperturbed spectrum).
+
+The FSR here is {Ω*1e-6:.2f}MHz as ascertained from the bath modes. According to the simulation for driving
+on the bath mode, we should see at least one hybridized mode where the red lorentzian is plotted. It's amplitude
+is fantasy, but there certainly there seems to be something there!
+"""
+)
+
+
+# %% save
+if __name__ == "__main__":
+    save_figure(fig, "005_11_07_analysis_on_bath")
+
+    quick_save_pickle(
+        dict(
+            window=window,
+            peak_info=peak_info,
+            ringdown_parms=ringdown_params,
+            Ω=Ω,
+        ),
+        "005_results",
+    )

scripts/ringdown_spectrum_analysis/figs/004_06_11_07_bhhb_best.pdf.meta.yaml

@@ -0,0 +1,8 @@
+change_id: rxrtsmmqspvtqxwvltyzqxntmowrvuxk
+commit_id: 27d6a0603bbd5f1af744cecebc9d34e9e817f417
+description: ''
+extra_meta: null
+function: <module>
+name: 004_06_11_07_bhhb_best
+refers_to: ./figs/004_06_11_07_bhhb_best.pdf
+source: scripts/ringdown_spectrum_analysis/004_simulation_11_07.py

scripts/ringdown_spectrum_analysis/figs/005_11_07_analysis_on_bath.pdf.meta.yaml

@@ -0,0 +1,8 @@
+change_id: rxrtsmmqspvtqxwvltyzqxntmowrvuxk
+commit_id: 79ac3014d82cc3dbecb65c44ae375c44019795d3
+description: ''
+extra_meta: null
+function: <module>
+name: 005_11_07_analysis_on_bath
+refers_to: ./figs/005_11_07_analysis_on_bath.pdf
+source: scripts/ringdown_spectrum_analysis/005_11_07_analysis_on_bath.py

scripts/ringdown_spectrum_analysis/outputs/005_results.pkl.meta.yaml

@@ -0,0 +1,6 @@
+change_id: rxrtsmmqspvtqxwvltyzqxntmowrvuxk
+commit_id: e2b1c2a8ffea1ab07e45ab07b0a3a66d4ae6207f
+description: ''
+function: <module>
+refers_to: outputs/005_results.pkl
+source: scripts/ringdown_spectrum_analysis/005_11_07_analysis_on_bath.py

src/plot_utils.py

@@ -136,6 +136,8 @@ def write_meta(path, **kwargs):
 
 @noop_if_interactive
 def save_figure(fig, name, extra_meta=None, *args, **kwargs):
+    import pickle
+
     dir = pathlib.Path(f"./figs/")
     dir.mkdir(exist_ok=True)
     fig.tight_layout()
@@ -146,6 +148,10 @@ def save_figure(fig, name, extra_meta=None, *args, **kwargs):
     plt.savefig(f"./figs/{name}.png", *args, dpi=600, **kwargs)
 
     print(f"Figure saved as ./figs/{name}.pdf")
+    pickle_path = dir / f"{name}.pkl"
+
+    with open(pickle_path, "wb") as f:
+        pickle.dump(fig, f)
 
 
 @noop_if_interactive